{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TMY tutorial\n",
    "\n",
    "This tutorial shows how to use the ``pvlib.tmy`` module to read data from TMY2 and TMY3 files.\n",
    "\n",
    "This tutorial has been tested against the following package versions:\n",
    "* pvlib 0.3.0\n",
    "* Python 3.5.1\n",
    "* IPython 4.1\n",
    "* pandas 0.18.0\n",
    "\n",
    "Authors:\n",
    "* Will Holmgren (@wholmgren), University of Arizona. July 2014, July 2015, March 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# built in python modules\n",
    "import datetime\n",
    "import os\n",
    "import inspect\n",
    "\n",
    "# python add-ons\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# plotting libraries\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "try:\n",
    "    import seaborn as sns\n",
    "except ImportError:\n",
    "    pass\n",
    "\n",
    "import pvlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pvlib comes packaged with a TMY2 and a TMY3 data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the absolute file path to your pvlib installation\n",
    "pvlib_abspath = os.path.dirname(os.path.abspath(inspect.getfile(pvlib)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the TMY data using the functions in the ``pvlib.iotools`` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmy3_data, tmy3_metadata = pvlib.iotools.read_tmy3(os.path.join(pvlib_abspath, 'data', '703165TY.csv'))\n",
    "tmy2_data, tmy2_metadata = pvlib.iotools.read_tmy2(os.path.join(pvlib_abspath, 'data', '12839.tm2'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the TMY3 metadata and the first 5 lines of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'USAF': 703165, 'Name': '\"SAND POINT\"', 'State': 'AK', 'TZ': -9.0, 'latitude': 55.317, 'longitude': -160.517, 'altitude': 7.0}\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date (MM/DD/YYYY)</th>\n",
       "      <th>Time (HH:MM)</th>\n",
       "      <th>ETR</th>\n",
       "      <th>ETRN</th>\n",
       "      <th>GHI</th>\n",
       "      <th>GHISource</th>\n",
       "      <th>GHIUncertainty</th>\n",
       "      <th>DNI</th>\n",
       "      <th>DNISource</th>\n",
       "      <th>DNIUncertainty</th>\n",
       "      <th>...</th>\n",
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       "      <th>LprecipUncertainty</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1997-01-01 01:00:00-09:00</th>\n",
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       "      <td>8</td>\n",
       "      <td>0.24</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>-9900</td>\n",
       "      <td>-9900</td>\n",
       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01 04:00:00-09:00</th>\n",
       "      <td>01/01/1997</td>\n",
       "      <td>04:00</td>\n",
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       "      <td>8</td>\n",
       "      <td>0.24</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>-9900</td>\n",
       "      <td>-9900</td>\n",
       "      <td>?</td>\n",
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       "    <tr>\n",
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       "</table>\n",
       "<p>5 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Date (MM/DD/YYYY) Time (HH:MM)  ETR  ETRN  GHI  \\\n",
       "1997-01-01 01:00:00-09:00        01/01/1997        01:00    0     0    0   \n",
       "1997-01-01 02:00:00-09:00        01/01/1997        02:00    0     0    0   \n",
       "1997-01-01 03:00:00-09:00        01/01/1997        03:00    0     0    0   \n",
       "1997-01-01 04:00:00-09:00        01/01/1997        04:00    0     0    0   \n",
       "1997-01-01 05:00:00-09:00        01/01/1997        05:00    0     0    0   \n",
       "\n",
       "                           GHISource  GHIUncertainty  DNI  DNISource  \\\n",
       "1997-01-01 01:00:00-09:00          1               0    0          1   \n",
       "1997-01-01 02:00:00-09:00          1               0    0          1   \n",
       "1997-01-01 03:00:00-09:00          1               0    0          1   \n",
       "1997-01-01 04:00:00-09:00          1               0    0          1   \n",
       "1997-01-01 05:00:00-09:00          1               0    0          1   \n",
       "\n",
       "                           DNIUncertainty  ...    AOD  AODSource  \\\n",
       "1997-01-01 01:00:00-09:00               0  ...  0.051          F   \n",
       "1997-01-01 02:00:00-09:00               0  ...  0.051          F   \n",
       "1997-01-01 03:00:00-09:00               0  ...  0.051          F   \n",
       "1997-01-01 04:00:00-09:00               0  ...  0.051          F   \n",
       "1997-01-01 05:00:00-09:00               0  ...  0.051          F   \n",
       "\n",
       "                           AODUncertainty   Alb  AlbSource  AlbUncertainty  \\\n",
       "1997-01-01 01:00:00-09:00               8  0.24          F               8   \n",
       "1997-01-01 02:00:00-09:00               8  0.24          F               8   \n",
       "1997-01-01 03:00:00-09:00               8  0.24          F               8   \n",
       "1997-01-01 04:00:00-09:00               8  0.24          F               8   \n",
       "1997-01-01 05:00:00-09:00               8  0.24          F               8   \n",
       "\n",
       "                           Lprecipdepth  Lprecipquantity  LprecipSource  \\\n",
       "1997-01-01 01:00:00-09:00         -9900            -9900              ?   \n",
       "1997-01-01 02:00:00-09:00         -9900            -9900              ?   \n",
       "1997-01-01 03:00:00-09:00         -9900            -9900              ?   \n",
       "1997-01-01 04:00:00-09:00         -9900            -9900              ?   \n",
       "1997-01-01 05:00:00-09:00         -9900            -9900              ?   \n",
       "\n",
       "                           LprecipUncertainty  \n",
       "1997-01-01 01:00:00-09:00                   0  \n",
       "1997-01-01 02:00:00-09:00                   0  \n",
       "1997-01-01 03:00:00-09:00                   0  \n",
       "1997-01-01 04:00:00-09:00                   0  \n",
       "1997-01-01 05:00:00-09:00                   0  \n",
       "\n",
       "[5 rows x 68 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(tmy3_metadata)\n",
    "tmy3_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmy3_data['GHI'].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The TMY readers have an optional argument to coerce the year to a single value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmy3_data, tmy3_metadata = pvlib.iotools.read_tmy3(os.path.join(pvlib_abspath, 'data', '703165TY.csv'), coerce_year=1987)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmy3_data['GHI'].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the TMY2 data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'WBAN': '12839', 'City': 'MIAMI', 'State': 'FL', 'TZ': -5, 'latitude': 25.8, 'longitude': -80.26666666666667, 'altitude': 2.0}\n",
      "                           year  month  day  hour  ETR  ETRN  GHI GHISource  \\\n",
      "1962-01-01 00:00:00-05:00  62.0    1.0  1.0   1.0  0.0   0.0  0.0         ?   \n",
      "1962-01-01 01:00:00-05:00  62.0    1.0  1.0   2.0  0.0   0.0  0.0         ?   \n",
      "1962-01-01 02:00:00-05:00  62.0    1.0  1.0   3.0  0.0   0.0  0.0         ?   \n",
      "1962-01-01 03:00:00-05:00  62.0    1.0  1.0   4.0  0.0   0.0  0.0         ?   \n",
      "1962-01-01 04:00:00-05:00  62.0    1.0  1.0   5.0  0.0   0.0  0.0         ?   \n",
      "\n",
      "                           GHIUncertainty  DNI  ... PwatUncertainty   AOD  \\\n",
      "1962-01-01 00:00:00-05:00             0.0  0.0  ...             8.0  62.0   \n",
      "1962-01-01 01:00:00-05:00             0.0  0.0  ...             8.0  62.0   \n",
      "1962-01-01 02:00:00-05:00             0.0  0.0  ...             8.0  62.0   \n",
      "1962-01-01 03:00:00-05:00             0.0  0.0  ...             8.0  62.0   \n",
      "1962-01-01 04:00:00-05:00             0.0  0.0  ...             8.0  62.0   \n",
      "\n",
      "                           AODSource AODUncertainty  SnowDepth  \\\n",
      "1962-01-01 00:00:00-05:00          F            8.0        0.0   \n",
      "1962-01-01 01:00:00-05:00          F            8.0        0.0   \n",
      "1962-01-01 02:00:00-05:00          F            8.0        0.0   \n",
      "1962-01-01 03:00:00-05:00          F            8.0        0.0   \n",
      "1962-01-01 04:00:00-05:00          F            8.0        0.0   \n",
      "\n",
      "                           SnowDepthSource SnowDepthUncertainty  LastSnowfall  \\\n",
      "1962-01-01 00:00:00-05:00                A                  7.0          88.0   \n",
      "1962-01-01 01:00:00-05:00                A                  7.0          88.0   \n",
      "1962-01-01 02:00:00-05:00                A                  7.0          88.0   \n",
      "1962-01-01 03:00:00-05:00                A                  7.0          88.0   \n",
      "1962-01-01 04:00:00-05:00                A                  7.0          88.0   \n",
      "\n",
      "                           LastSnowfallSource LastSnowfallUncertaint  \n",
      "1962-01-01 00:00:00-05:00                   E                    7.0  \n",
      "1962-01-01 01:00:00-05:00                   E                    7.0  \n",
      "1962-01-01 02:00:00-05:00                   E                    7.0  \n",
      "1962-01-01 03:00:00-05:00                   E                    7.0  \n",
      "1962-01-01 04:00:00-05:00                   E                    7.0  \n",
      "\n",
      "[5 rows x 70 columns]\n"
     ]
    }
   ],
   "source": [
    "print(tmy2_metadata)\n",
    "print(tmy2_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
